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An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet

by Zeinab Hassani, Aylin Pakzad, Arezou Asghari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 41
Year of Publication: 2018
Authors: Zeinab Hassani, Aylin Pakzad, Arezou Asghari
10.5120/ijca2018916983

Zeinab Hassani, Aylin Pakzad, Arezou Asghari . An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet. International Journal of Computer Applications. 179, 41 ( May 2018), 31-38. DOI=10.5120/ijca2018916983

@article{ 10.5120/ijca2018916983,
author = { Zeinab Hassani, Aylin Pakzad, Arezou Asghari },
title = { An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 179 },
number = { 41 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number41/29357-2018916983/ },
doi = { 10.5120/ijca2018916983 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:58:05.238799+05:30
%A Zeinab Hassani
%A Aylin Pakzad
%A Arezou Asghari
%T An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 41
%P 31-38
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet is one of the most influential new communications technologies that have been affected both social and health aspects of users’ lives. Despite many advantages and its positive pointes in communication, the excessive use of Internet make a serious danger that has adverse impacts on overall health. The inability of individuals to control the Internet use has been defined as Internet Addiction (IA). There are a lot of studies on IA over the topics of diagnosis, phenomenology, epidemiology, and treatment based on statistical analysis, but a few research works are conducted by machine learning techniques. So our research focuses on diagnosing and counseling to prevent IA by developing prediction models based on ensemble method with machine learning algorithms and finding the most important risk factors in diagnosing the status of users’ IA. Experiment results demonstrate that this hybrid machine learning model by considering students field and location along with items 1, 3, 4, 5, 10, 12, 14, 15, 19, 20 from Internet Addiction Test (IAT) as the most important risk factors can identify the status of users’ IA with 99.75% accuracy attains a performance superior to traditional single classifier systems.

References
  1. Goldberg, I. 1996. “Internet Addiction Disorder.” Retrieved February 10.2009 from http:// www.rider.edu/~suler/psycber/supportgp.html.
  2. Young, K. S. 1998a. Internet addiction: The emergence of a new clinical disorder. Cyberpsychology and Behavior, 1(3), 237–244. http://dx.doi.org/10.1089/cpb.1998.1. 237.
  3. Young, K. 1996. Psychology of computer use: XL. Addictive use of the Internet: A case that breaks the stereotype. Psychological Reports, 79, 899- 902.
  4. Young, K. S. 1998b. Caught in the net: How to recognize the signs of internet addiction and a winning strategy for recovery. John Wiley & Sons.
  5. Bayraktar, F. 2001. “İnternet kullanımının ergen gelisimindeki rolu”. Unpublished PHD thesis, Ege Universitesi Sosyal Bilimler Enstitusu, İzmir.
  6. Lin, S. S., and Tsai, C. C. 2002. “Sensation seeking and internet dependence of Taiwanese high school adolescents.” Computers in human behavior, 18(4), 411-426.
  7. Block, J. J. 2008. Issues for DSM-V: Internet Addiction, THE AMERICAN JOURNAL OF PSYCHIATRY, Volume 165 Number 3, 306-307.
  8. Scimeca, G., Bruno, A., Cava, L., Pandolfo, G., Muscatello, M. R. A., and Zoccali, R. 2014. The relationship between alexithymia, anxiety, depression, and internet addiction severity in a sample of Italian high school students. Scientific World Journal, 2014. http://dx.doi.org/10.1155/2014/504376.
  9. Frangos, C. C., Fragkos, K. C., and Kiohos, A. 2010. Internet Addiction among Greek University Students: Demographic Associations with the Phenomenon, Using the Greek Version of Young’s Internet Addiction Test. International Journal of Economic Sciences and Applied Research. 3(1), 49
  10. Young, K. S. 2004. Internet addiction: A new clinical phenomenon and its consequences. The American Behavioral Scientist, 48(4), 402–415. http://dx.doi.org/10.1177/ 0002764204270278.
  11. Shek, D. T. L. and Leung, H. 2013. “Development of an integrated intervention model for Internet addiction in Hong Kong”, International Journal of Child and Adolescent Health, 6(4), pp. 475-486.
  12. Shek, D. T. L. and Yu, L. 2013. “Internet addiction phenomenon in early adolescents in Hong Kong”, International Journal of Child Health and Human Development, 6(1), pp. 145- 156.
  13. Van Rooij, A. J., and Prause, N. 2014. A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3, 203-213
  14. June, K. J., Sohn, S. Y., So, A. Y., Yi, G. M., and Park, S. H. 2007. A study of factors that influence Internet addiction, smoking, and drinking in high school students. Taehan Kanho Hakhoe Chi.  37(6):872-82.
  15. Ferraro, G., Caci, B., D'Amico, D., and Blasi, M. D. 2007. Internet Addiction Disorder: An Italian Study, CyberPsychology & BehaviorVol. 10, No. 2, 170-175.
  16. Tekkanat, E., and Topaloglu, M. 2015. The Assessment of High Schoolers’ Internet Addiction. Procedia - Social and Behavioral Sciences 205, 664 – 670.
  17. Widyanto, L., and McMurran, M. 2004. The psychometric properties of the internet addiction test. Cyberpsychology Behaviour, 7(4), 443–450. http://dx.doi.org/10.1089/ cpb.2004.7.443.
  18. Alavi, S. S., Eslami, M., Meracy, M. R., Najafi, M., Jannatifard, F., and Rezapour, H. 2010. Psychometric properties of Young internet addiction test. International Journal of Behavioral Science, 4(3), 183–189.
  19. Chang, M. K., and Man Law, S. P. 2008. Factor structure for Young's Internet Addiction Test: A confirmatory study. Computers in Human Behavior, 24(6), 2597–2619. http:// dx.doi.org/10.1016/j.chb.2008.03.001.
  20. Jelenchick, L. A., Becker, T., and Moreno, M. A. 2012. Assessing the psychometric properties of the Internet Addiction Test (IAT) in US college students. Psychiatry Research, 196, 296–301.
  21. Widyanto, L., Griffiths, M. D., and Brunsden, V. A. 2011. Psychometric Comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and Self-Diagnosis. Cyberpsychology, Behavior, and Social Networking, 14, 141–9.
  22. Murali, V., and George, S. 2007. An overview of internet addiction. Advances in Psychiatric Treatment, 13, 24-30.
  23. Zheng. Z. 2015. oversampling method for imbalanced classification computing and Informatics, Vol. 34, 1017–1037
  24. Skryjomski, P. L., and Krawczyk, B. 2017. Influence of minority class instance types on SMOTE imbalanced data oversampling, Proceedings of Machine Learning Research, 74,7–21.
  25. Kennedy, J. and Eberhart, R. 1995. Particle Swarm OptimizationIn: Proceedings of the 1995 IEEE international conference on neural networks. New Jersey: IEEE Press. 1942–1948.
  26. Kumar, A., Kumar Singh, B., and Patro, B. D. K. 2016. Particle Swarm Optimization: A Study of Variants and Their Applications, International Journal of Computer Applications, 135(5), (0975 – 8887).
  27. Astuty., and Haryono, T. 2016. Novel binary PSO algorithm based optimization of transmission expansion planning considering power losses, International Conference on Innovation in Engineering and Vocational Education, 128, 012023.
  28. Teng, X., Dong, H., and Zhou, X. 2017. Adaptive feature selection using v-shaped binary particle swarm optimization. PLoS ONE 12(3): e0173907.
  29. Yang, X.S. 2008. Nature-inspired Metaheuristic Algorithm. University of Cambridge. United Kingdom: Luniver Press.
  30. Yang, X. 2010. Engineering optimization: an introduction with metaheuristic applications.
  31. Liu, H., Yu, L. 2005. Toward integrating feature selection algorithms for classification and clustering, IEEE Transactions on Knowledge and Data Engineering, 17 (4), 491–502.
  32. Chandrasekaran, K., Simon, S. P. 2012. Network and reliability constrained unit commitment problem using binary real coded firefly algorithm, Electrical Power and Energy Systems, 43, 921–932.
  33. Fister, I., et al. 2013. Memetic Self-Adaptive Firefly Algorithm. Swarm Intelligence and Bio-Inspired Computation. 73-102.
  34. Palit, S., Sinha, S.N., Moll, M.A., Khanra, A., and Kule, M. 2011 A Cryptanalytic attack on the Knapsack Cryptosystem using Binary Firefly Algorithm, International Conference on Computer and Communication Technology (ICCCT), 317-320.
  35. Alchamlat, S. A., and Farnir, F. 2017. KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies, BMC Bioinformatics, 18:184
  36. Muayad, A., and Irtefaa, A. N. 2016. Ridge Regression using Artifiial Neural Network. Indian Journal of Science and Technology, 9(31).
  37. Kouchakzade, M., Bahmani, A. 2006. Assessment of artificial neural networks revenue in reducing required parameters for estimation of reference evapotranspiration. Journal of agricultural sciences Islamic Azad University, 11(4).
  38. Bilalli, B., Abello, A., Aluja-Banet, T., Wrembel, R. 2018. PRESISTANT: Learning based assistant for data pre-processing, Preprint submitted to Elsevier March 6.
  39. Atiya, A.F. 2001. Bankruptcy Prediction for Credit Risk Using Neural Network: A Survey and New Results. IEEE Trans on Neural Networks, 12 (4): 929-935.
  40. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., and Tourassi, G. D. 2008. “Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance,” Neural Netw., vol. 21, no. 2–3, pp. 427–436.
  41. Blum, A.L., and Langley, P. 1997. Selection of relevant features and examples in machine learning, Artificial Intelligence, 97 (1), 245–271.
  42. De Stefano, C., Fontanella, F., Marrocco, C., and Scotto di Freca, A. 2014. A GA-based feature selection approach with an application to handwritten character recognition, Pattern Recognition Letters, 35, 130-141.
  43. Deniz, A., Ezgi Kiziloz, H., Dokeroglu, T., Cosar, A. 2017). Robust multiobjective evolutionary feature subset selection algorithm for binary classification using machine learning techniques, Neurocomputing, 1-19.
Index Terms

Computer Science
Information Sciences

Keywords

Internet Addiction Machine Learning Particle Swarm Optimization Firefly Algorithm K nearest Neighbor.

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